python之如何使用经过训练的 Tensorflow 模型进行预测

我已经创建并训练了一个神经网络，但我希望能够输入测试点并查看其结果(而不是使用 eval 函数)。

模型运行良好，每个时期的成本都会降低，但我只想在最后添加一行来传递一些输入坐标，并让它告诉我预测的变换坐标。

import tensorflow as tf 
import numpy as np 
 
def coordinate_transform(size, angle): 
    input = np.random.rand(size, 2) 
    output = np.zeros((size, 2)) 
    noise = 0.05*(np.add(np.random.rand(size) * 2, -1)) 
    theta = np.add(np.add(np.arctan(input[:,1] / input[:,0]) , angle) , noise) 
    radii = np.sqrt(np.square(input[:,0]) + np.square(input[:,1])) 
    output[:,0] = np.multiply(radii, np.cos(theta)) 
    output[:,1] = np.multiply(radii, np.sin(theta)) 
    return input, output 
 
#Data 
input, output = coordinate_transform(2000, np.pi/2) 
train_in = input[:1000] 
train_out = output[:1000] 
test_in = input[1000:] 
test_out = output[1000:] 
 
# Parameters 
learning_rate = 0.001 
training_epochs = 15 
batch_size = 1 
display_step = 1 
 
# Network Parameters 
n_hidden_1 = 100 # 1st layer number of features 
n_input = 2 # [x,y] 
n_classes = 2 # output x,y coords 
 
# tf Graph input 
x = tf.placeholder("float", [1,n_input]) 
y = tf.placeholder("float", [1, n_input]) 
 
# Create model 
def multilayer_perceptron(x, weights, biases): 
    # Hidden layer with RELU activation 
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) 
    layer_1 = tf.nn.relu(layer_1) 
    # Output layer with linear activation 
    out_layer = tf.matmul(layer_1, weights['out']) + biases['out'] 
    return out_layer 
 
# Store layers weight & bias 
weights = { 
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 
    'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes])) 
} 
biases = { 
    'b1': tf.Variable(tf.random_normal([n_hidden_1])), 
    'out': tf.Variable(tf.random_normal([n_classes])) 
} 
 
# Construct model 
pred = multilayer_perceptron(x, weights, biases) 
 
# Define loss and optimizer 
#cost = tf.losses.mean_squared_error(0, (tf.slice(pred, 0, 1) - x)**2 + (tf.slice(pred, 1, 1) - y)**2) 
cost = tf.losses.mean_squared_error(y, pred) 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) 
optimizer = optimizer.minimize(cost) 
 
# Initializing the variables 
#init = tf.global_variables_initializer() 
init = tf.initialize_all_variables() 
 
# Launch the graph 
with tf.Session() as sess: 
    sess.run(init) 
 
    # Training cycle 
    for epoch in range(training_epochs): 
        avg_cost = 0. 
        total_batch = 1000#int(len(train_in)/batch_size) 
        # Loop over all batches 
        for i in range(total_batch): 
            batch_x = train_in[i].reshape((1,2)) 
            batch_y = train_out[i].reshape((1,2)) 
 
            #print(batch_x.shape) 
            #print(batch_y.shape) 
            #print(batch_y, batch_x) 
            # Run optimization op (backprop) and cost op (to get loss value) 
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, 
                                                          y: batch_y}) 
            # Compute average loss 
            avg_cost += c / total_batch 
        # Display logs per epoch step 
        if epoch % display_step == 0: 
            print ("Epoch:", '%04d' % (epoch+1), "cost=", \ 
                "{:.9f}".format(avg_cost)) 
    print("Optimization Finished!") 
 
    # Test model 
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
    # Calculate accuracy 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
 
    #Make predictions 

请您参考如下方法:

好吧，'pred' 操作是您的实际结果(因为它在计算损失时用于与 y 进行比较)，所以像下面这样的东西应该可以解决问题:

print(sess.run([pred], feed_dict={x: _INPUT_GOES_HERE_ }) 

显然 _INPUT_GOES_HERE_ 需要用实际输入替换。